1. Technical Field
This invention relates to image processing and in particular to a system and method for processing digital images to detect and extract physical entities or objects represented in the image.
2. Related Art
Image segmentation is of fundamental importance to many digital image-processing applications. The process of image segmentation refers to the grouping together of parts of an image that have similar image characteristics and this is often the first process in image processing tasks. For instance, in the field of video coding it is often desirable to decompose an image into an assembly of its constituent object components prior to coding. This pre-processing step of image segmentation then allows individual objects to be coded separately. Hence, significant data compression can be achieved in video sequences since slow moving background can be transmitted less frequently than faster moving objects.
Image segmentation is also important in the field of image enhancement, particularly in medical imaging such as radiography. Image segmentation can be used to enhance detail contained in an image in order to improve the usefulness of the image. For instance, filtering methods based on segmentation have been developed for removing noise and random variations in intensity and contrast from captured digital images to enhance image detail and assist human visualisation and perception of the image contents.
Other fields where image segmentation is important include multi-media applications such as video indexing and post production content-based image retrieval and interpretation, that is to say video sequence retrieval based on user supplied content parameters and machine recognition and interpretation of image contents based on such parameters.
Fundamental to image segmentation is the detection of homogeneous regions and/or the boundaries of such regions which represent objects in that image. Homogeneity may be detected in terms of intensity or texture, that is grey level values, motion (for video sequences), disparity (for stereoscopic images), colour, and/or focus for example. Many approaches to image segmentation have been attempted including texture-based, intensity-based, motion-based and focus-based segmentation. Known approaches require significant computational resources and often provide unsatisfactory results.
One approach that uses intensity or grey level values for object segmentation is thresholding. The concept of image segmentation based on thresholding is described in the paper “An Amplitude Segmentation Method Based on the Distribution Function of an Image”, Compute, Vision, Graphics and Image Processing, 29, 47–59, 1985. In the thresholding method intensity values are determined for each pixel or picture element in a digital image and on the basis of these values a threshold value is determined that distinguishes each pixel of an object in the image from pixels representing background detail. In practice, the threshold intensity value is determined dynamically for each image according to the statistical distribution of intensity values, that is to say, the value is based on a histogram analysis of all the intensity values for a particular image. Peaks in the histogram distribution generally represent intensity values predominately associated with a particular object. If two objects are present in an image there will be two peaks. In these circumstances the intersection or overlap between the two peaks is taken as the threshold value. This approach to image segmentation is relatively straightforward but can be computationally intensive particularly when complex images are presented, for example, images comprising a number of objects or complex backgrounds or when the image is heavily “textured”, that is to say, the image comprises a number separate regions within an object that have different intensity values. When textured images are processed using threshold-based methods “over-segmentation” can occur, that is, regions within an object are themselves recognised as separate objects within the image being processed.
The problem of over segmentation can be partially overcome if the image is simplified prior to thresholding. Image simplification involves the removal of low order intensity value differences between adjacent pixels within an object boundary while the intensity value differences are maintained at the object boundaries. Image simplification is often achieved in digital image processing by using so called non-linear diffusion methods. The concept of non-linear diffusion for image processing is described in the published paper “Scale Space and Edge Detection Using Anisotropic Diffusion”, IEEE Trans. on Pattern Analysis and Machine Intelligence Vol. 12 No. 7 pp629–639, July 1990. In this method pixel intensities are altered in a manner analogous to diffusion of physical matter to provide regions of homogenous intensity within object boundaries while preventing diffusion at the object boundaries, thereby preserving intensity contrast at the boundaries. It has been found, however, that methods of image simplification based on known non-linear diffusion algorithms result in over segmentation.